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|
%global _empty_manifest_terminate_build 0
Name: python-fuzzy-match
Version: 0.0.1
Release: 1
Summary: Fuzzy string matching in Python
License: MIT
URL: https://github.com/darwinagain/fuzzy-match
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2f/88/7d2bc2d9d87d3c7437292def951ad8fbcb7956c3a8b9e250ec229623f01e/fuzzy-match-0.0.1.tar.gz
BuildArch: noarch
%description
# Fuzzy-Match
Fuzzy string matching in Python. By default it uses [Trigrams](https://en.wikipedia.org/wiki/Trigram) to calculate a similarity score and find matches by splitting strings into ngrams with a length of 3. The length of the ngram can be altered if desired. Also, [Cosine](https://en.wikipedia.org/wiki/Cosine_similarity), [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance), and [Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) algorithims are also available as alternatives.
# Usage
```python
>>> from fuzzy_match import match
>>> from fuzzy_match import algorithims
```
### Trigram
```python
>>> algorithims.trigram("this is a test string", "this is another test string")
0.703704
```
### Cosine
```python
>>> algorithims.cosine("this is a test string", "this is another test string")
0.7999999999999998
```
### Levenshtein
```python
>>> algorithims.levenshtein("this is a test string", "this is another test string")
0.7777777777777778
```
### Jaro-Winkler
```python
>>> algorithims.jaro_winkler("this is a test string", "this is another test string")
0.798941798941799
```
### Match
```python
>>> choices = ["simple strings", "strings are simple", "sim string", "string to match", "matching simple strings", "matching strings again"]
>>> match.extract("simple string", choices, limit=2)
[('simple strings', 0.8), ('sim string', 0.642857)]
>>> match.extractOne("simple string", choices)
('simple strings', 0.8)
```
You can also pass additional arguments to `extract` and `extractOne` to set a score cutoff value or use one of the other algorithims mentioned above. Here is an example:
```python
>>> match.extract("simple string", choices, match_type='levenshtein', score_cutoff=0.7)
[('simple strings', 0.9285714285714286), ('sim string', 0.7692307692307693)]
```
`match_type` options include `trigram`, `cosine`, `levenshtein`, `jaro_winkler`
%package -n python3-fuzzy-match
Summary: Fuzzy string matching in Python
Provides: python-fuzzy-match
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-fuzzy-match
# Fuzzy-Match
Fuzzy string matching in Python. By default it uses [Trigrams](https://en.wikipedia.org/wiki/Trigram) to calculate a similarity score and find matches by splitting strings into ngrams with a length of 3. The length of the ngram can be altered if desired. Also, [Cosine](https://en.wikipedia.org/wiki/Cosine_similarity), [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance), and [Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) algorithims are also available as alternatives.
# Usage
```python
>>> from fuzzy_match import match
>>> from fuzzy_match import algorithims
```
### Trigram
```python
>>> algorithims.trigram("this is a test string", "this is another test string")
0.703704
```
### Cosine
```python
>>> algorithims.cosine("this is a test string", "this is another test string")
0.7999999999999998
```
### Levenshtein
```python
>>> algorithims.levenshtein("this is a test string", "this is another test string")
0.7777777777777778
```
### Jaro-Winkler
```python
>>> algorithims.jaro_winkler("this is a test string", "this is another test string")
0.798941798941799
```
### Match
```python
>>> choices = ["simple strings", "strings are simple", "sim string", "string to match", "matching simple strings", "matching strings again"]
>>> match.extract("simple string", choices, limit=2)
[('simple strings', 0.8), ('sim string', 0.642857)]
>>> match.extractOne("simple string", choices)
('simple strings', 0.8)
```
You can also pass additional arguments to `extract` and `extractOne` to set a score cutoff value or use one of the other algorithims mentioned above. Here is an example:
```python
>>> match.extract("simple string", choices, match_type='levenshtein', score_cutoff=0.7)
[('simple strings', 0.9285714285714286), ('sim string', 0.7692307692307693)]
```
`match_type` options include `trigram`, `cosine`, `levenshtein`, `jaro_winkler`
%package help
Summary: Development documents and examples for fuzzy-match
Provides: python3-fuzzy-match-doc
%description help
# Fuzzy-Match
Fuzzy string matching in Python. By default it uses [Trigrams](https://en.wikipedia.org/wiki/Trigram) to calculate a similarity score and find matches by splitting strings into ngrams with a length of 3. The length of the ngram can be altered if desired. Also, [Cosine](https://en.wikipedia.org/wiki/Cosine_similarity), [Levenshtein Distance](https://en.wikipedia.org/wiki/Levenshtein_distance), and [Jaro-Winkler Distance](https://en.wikipedia.org/wiki/Jaro%E2%80%93Winkler_distance) algorithims are also available as alternatives.
# Usage
```python
>>> from fuzzy_match import match
>>> from fuzzy_match import algorithims
```
### Trigram
```python
>>> algorithims.trigram("this is a test string", "this is another test string")
0.703704
```
### Cosine
```python
>>> algorithims.cosine("this is a test string", "this is another test string")
0.7999999999999998
```
### Levenshtein
```python
>>> algorithims.levenshtein("this is a test string", "this is another test string")
0.7777777777777778
```
### Jaro-Winkler
```python
>>> algorithims.jaro_winkler("this is a test string", "this is another test string")
0.798941798941799
```
### Match
```python
>>> choices = ["simple strings", "strings are simple", "sim string", "string to match", "matching simple strings", "matching strings again"]
>>> match.extract("simple string", choices, limit=2)
[('simple strings', 0.8), ('sim string', 0.642857)]
>>> match.extractOne("simple string", choices)
('simple strings', 0.8)
```
You can also pass additional arguments to `extract` and `extractOne` to set a score cutoff value or use one of the other algorithims mentioned above. Here is an example:
```python
>>> match.extract("simple string", choices, match_type='levenshtein', score_cutoff=0.7)
[('simple strings', 0.9285714285714286), ('sim string', 0.7692307692307693)]
```
`match_type` options include `trigram`, `cosine`, `levenshtein`, `jaro_winkler`
%prep
%autosetup -n fuzzy-match-0.0.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-fuzzy-match -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.1-1
- Package Spec generated
|
